1. Technical Field
This disclosure generally relates to brain mapping utilizing magnetic resonance imaging (MRI). More particularly, this disclosure relates to diffusion tensor imaging (DTI) for real-time display of brain structures and regional segmentation of brain tissues.
2. Background
Magnetic Resonance Imaging (MRI), or nuclear magnetic resonance imaging, is a medical imaging technique most commonly used to visualize detailed internal structures in the body. MRI provides much greater contrast between the different soft tissues of the body than computed tomography (CT). Furthermore, unlike CT, MRI involves no ionizing radiation because it uses a powerful magnetic field to align protons, most commonly those of the hydrogen atoms of the water present in tissue. A radio frequency electromagnetic field is then briefly turned on, causing the protons to alter their alignment relative to the field. When this field is turned off the protons return to their original magnetization alignment. These alignment changes create signals which are detected by a scanner. Images can be created because the protons in different tissues return to their equilibrium state at different rates. By altering the parameters on the scanner this effect is used to create contrast between different types of body tissue. MRI is used to image every part of the body, and is particularly useful for neurological conditions, for disorders of the muscles and joints, for evaluating tumors, and for showing abnormalities in the heart and blood vessels. Magnetic resonance imaging (MRI) methods provide several tissue contrast mechanisms that can be used to assess the micro- and macrostructure of living tissue in both health and disease. Diffusion MRI is a method that produces in vivo images of biological tissues weighted with the local microstructural characteristics of water diffusion. There are two distinct forms of diffusion MRI, diffusion weighted MRI and diffusion tensor MRI.
In diffusion weighted imaging (DWI), each image voxel (three dimensional pixel) has an image intensity that reflects a single best measurement of the rate of water diffusion at that location. This measurement is more sensitive to early changes such as occur after a stroke than more traditional MRI measurements such as T1 or T2 relaxation rates. DWI is most applicable when the tissue of interest is dominated by isotropic water movement e.g. grey matter in the cerebral cortex and major brain nuclei—where the diffusion rate appears to be the same when measured along any axis. Traditionally, in diffusion-weighted imaging (DWI), three gradient-directions are applied, sufficient to estimate the trace of the diffusion tensor or ‘average diffusivity’, a putative measure of edema. Clinically, trace-weighted images have proven to be very useful to diagnose vascular strokes in the brain, by early detection (within a couple of minutes) of the hypoxic edema.
Diffusion tensor imaging (DTI) is a technique that enables the measurement of the restricted diffusion of water in tissue in order to produce neural tract images instead of using this data solely for the purpose of assigning contrast or colors to pixels in a cross sectional image. It also provides useful structural information about muscle—including heart muscle, as well as other tissues such as the prostate. DTI is important when a tissue—such as the neural axons of white matter in the brain or muscle fibers in the heart—has an internal fibrous structure analogous to the anisotropy of some crystals. Water will then diffuse more rapidly in the direction aligned with the internal structure, and more slowly as it moves perpendicular to the preferred direction. This also means that the measured rate of diffusion will differ depending on the direction from which an observer is looking. In DTI, each voxel therefore has one or more pairs of parameters: a rate of diffusion and a preferred direction of diffusion, described in terms of three dimensional space, for which that parameter is valid. The properties of each voxel of a single DTI image is usually calculated by vector or tensor math from six or more different diffusion weighted acquisitions, each obtained with a different orientation of the diffusion sensitizing gradients. In some methods, hundreds of measurements—each making up a complete image—are made to generate a single resulting calculated image data set. The higher information content of a DTI voxel makes it extremely sensitive to subtle pathology in the brain. In addition the directional information can be exploited at a higher level of structure to select and follow neural tracts through the brain—a process called tractography.
More extended diffusion tensor imaging (DTI) scans derive neural tract directional information from the data using 3D or multidimensional vector algorithms based on three, six, or more gradient directions, sufficient to compute the diffusion tensor. The diffusion model is a rather simple model of the diffusion process, assuming homogeneity and linearity of the diffusion within each image voxel. From the diffusion tensor, diffusion anisotropy measures such as the fractional anisotropy (FA) can be computed. Moreover, the principal direction of the diffusion tensor can be used to infer the white-matter connectivity of the brain (i.e. tractography; trying to see which part of the brain is connected to which other part). Recently, more advanced models of the diffusion process have been proposed that aim to overcome the weaknesses of the diffusion tensor model. Amongst others, these include q-space imaging and generalized diffusion tensor imaging.
There are several applications for bran analysis that can benefit from the availability of robust methods for estimating cortical and subcortical gray matter (GM) volume and their corresponding quantitative relaxation or diffusion tensor metrics (Fjell A M, Westlye L T, Greve D N, Fischl B, Benner T, van der Kouwe A J, Salat D, Bjørnerud A, Due-Tønnessen P, Walhovd K B (2008): NeuroImage 42:1654-1668; Hasan K M, Kamali A, Kramer L A (2009a). Mapping the human brain white matter tracts relative to cortical and deep gray matter using diffusion tensor imaging at high spatial resolution. Magn Reson Imaging (doi: 10.1016/j.mri.2008.10.007); Lawes I N, Barrick T R, Murugam V, Spierings N, Evans D R, Song M, Clark C A (2008): Atlas-based segmentation of white matter tracts of the human brain using diffusion tensor tractography and comparison with classical dissection. NeuroImage 39:62-79; Mabbott D J, Noseworthy M, Bouffet E, Laughlin S, Rockel C (2006): White matter growth as a mechanism of cognitive development in children. NeuroImage 33:936-946; Makris N, Papadimitriou G M, Sorg S, Kennedy D N, Caviness V S, Pandya D N (2007): The occipitofrontal fascicle in humans: a quantitative, in vivo, DT-MRI study. NeuroImage 37:1100-1111; Wakana S, Jiang H, Nagae-Poetscher L M, van Zijl P C, Mori S (2004): Fiber tract-based atlas of human white matter anatomy. Radiology 23:77-87).
In general, current MRI methods for tissue volume assessment use high spatial resolution T1-weighted, or multi-modal T2-weighted, fluid-attenuated and proton density volumes for regional tissue segmentation. Tissue segmentation using T1- or T2-weighted volumes require image intensity correction (Ahsan R L, Allom R, Gousias I S, Habib H, Turkheimer F E, Free S, Lemieux L, Myers R, Duncan J S, Brooks, D J, Koepp M J, Hammers A (2007): Volumes, spatial extents and a probabilistic atlas of the human basal ganglia and thalamus. NeuroImage 38:261-270) while multi-modal MRI methods requires coalignment of all data sets before segmentation (Liu T, Young G, Huang L, Chen N K, Wong S T (2006): 76-space analysis of grey matter diffusivity: methods and applications. Neuroimage 31:51-65; Ali A A, Dale A M, Badea A, Johnson G A (2005): Automated segmentation of neuroanatomical structures in multispectral MR microscopy of the mouse brain. NeuroImage 27:425-435; Hasan K M, Halphen C, Boska M D, Narayana P A (2008a): Diffusion tensor metrics, T2 relaxation, and volumetry of the naturally aging human caudate nuclei in healthy young and middle-aged adults: possible implications for the neurobiology of human brain aging and disease. Magn Reson Med 59:7-13; Pham D L, Xu C, Prince J L (2000): Current methods in medical image segmentation. Annu. Rev. Biomed. Eng. 2:315-337).
To obtain intrinsic tissue relaxation or DTI metrics from certain manually or automatically segmented regions, the acquisition of separate data sets is needed along with perfect multimodal data coregistration and fusion with the T1-weighted volume (Mabbott D J, Noseworthy M, Bouffet E, Laughlin S, Rockel C (2006): White matter growth as a mechanism of cognitive development in children. NeuroImage 33:936-946; Thottakara P, Lazar M, Johnson S C, Alexander A L (2006): Application of Brodmann's area templates for ROI selection in white matter tractography studies. NeuroImage 29:868-878).
The DTI-based tissue contrast method was used to obtain whole brain cerebrospinal fluid (CSF), GM, and white matter (WM) volumes from typically developing children (Hasan K M, Halphen C, Sankar A, Eluvathingal T J, Kramer L, Stuebing K K, Fletcher J M, Ewing-Cobbs L (2007a): Diffusion tensor imaging-based tissue segmentation: validation and application to the developing child and adolescent brain. NeuroImage 34:1497-1505). This DTI-based method was applied to obtain well-documented developing and aging trends of whole brain CSF, GM and WM across the human lifespan (Hasan K M, Sankar A, Halphen C, Kramer L A, Brandt M E, Juranek J, Cirino, P T, Fletcher J M, Papanicolaou A C, Ewing-Cobbs L (2007b): Development and organization of the human brain tissue compartments across the lifespan using diffusion tensor imaging. Neuroreport 18:1735-1739). These DTI-based segmentation methods were validated and extended further to the semi-automated segmentation of WM regions such as the corpus callosum (Hasan K M, Ewing-Cobbs L, Kramer L A, Fletcher J M, Narayana P A (2008b): Diffusion tensor quantification of the macrostructure and microstructure of human midsagittal corpus callosum across the lifespan. NMR Biomed 21:1094-1101; Hasan K M, Kamali A, Kramer L A, Papanicolaou A C, Fletcher J M, Ewing-Cobbs L (2008c): Diffusion tensor quantification of the human midsagittal corpus callosum subdivisions across the lifespan. Brain Research 1227:52-67).
MRI
Magnetic resonance imaging (MRI) is based on imaging water rich soft central nervous tissue. The MRI data acquisition involves water spin polarization or alignment in a strong magnetic field and then the application of timed and controlled spatially dependent magnetic pulses for spatial encoding (FIG. 1). The signal is collected using a radio-frequency tuned near-field coil and then amplified, decoded and visualized to show the water density maps. The MRI contrast can be used to differentiate different tissue types (e.g. gray matter, myelinated white matter and cerebrospinal fluid or abnormal tissue (e.g. demyelination, tumors, and infarcts).
DTI or DTMRI
Diffusion tensor imaging (DTI) or diffusion tensor magnetic resonance imaging (DTMRI) uses the same MRI data acquisition and processing (Basser and Jones 2002). In addition to the standard MRI acquisition paradigm, strong diffusion magnetic pulses (Gx, Gy, Gz) or (gx, gy, gz) are applied along the three gradient channels to obtain diffusion-weighted or contrasted data (FIG. 2).
DTI Contrast
The main principle of diffusion contrast is that water molecular random translational motion will be hindered when water molecules encounter obstacles such as myelinated or compacted tissue. The application of a set of encoding directions with a number greater than six will enable the encoding of the three principal orientations to obtain the diffusion tensor (Basser et al. 1997). The diffusion tensor provides both scalar metrics such as anisotropy and diffusivity in addition to tissue local orientation to quantify and map the microstructural integrity and/or connectivity in the living tissue (FIG. 3).
Diffusion Tensor Analysis Pipeline
The raw or encoded diffusion-weighted data collected undergoes several preprocessing steps before display. These steps include image distortion correction that results from eddy currents upon using large magnetic pulses and then tensor decoding and diagonalization to obtain the eigenvalues and eigenvectors. Conventionally, the eigenvalues are used to compute fractional anisotropy and mean diffusivity (FIG. 4).
The ability to segment whole brain cerebrospinal fluid (CSF) and gray and white matter tissue to provide regional volume and DTI metrics of white matter tract and cortical and subcortical gray matter is important in many clinical applications. Such high resolution brain imaging is needed for accurate detection of congenital defects, diagnosis and determining prognosis of many neurologic disorders, such as, but not limited to brain tumors, traumatic brain injury, Alzheimer's disease, Parkinson's disease, Huntington's disease, swelling of the brain, such as, but not limited to that due to infectious diseases such as meningitis, viral or parasitic diseases. Therefore, there is continuing interest to develop methods and systems in DTI to provide such information.